ACIT4510 Statistical Learning Course description

The course covers the foundations and recent advances in Machine Learning from the point of view of Statistical Learning Theory. The goal of this course is to provide students with the practical skills to support the theoretical knowledge to (1) develop machine learning solutions to challenging problems and (2) to be able to develop the acquired expertise further.

The theoretical aspects of statistical learning will be illustrated with concrete problems and tasks in Python.

Two internal examiners. External examiner is used periodically.

No formal requirements over and above the admission requirements.

The student should have the following outcomes upon completing the course:

Knowledge

Upon successful completion of the course, the student:

• will have a good understanding the different concepts and methods of supervised and unsupervised statistical learning and how to apply them on large data.
• has advanced knowledge of probabilistic formulation of the various learning problems.
• has focused knowledge of theoretical aspects of the different methods in machine learning and statistical learning, as well as a deep knowledge of concepts and assumptions behind each method.

Skills

Upon successful completion of the course, the student:

• can apply different high-dimensional regression techniques on data
• can apply different classification techniques on data
• can apply clustering techniques on data
• can apply dimension reduction techniques on data
• can make informed decisions on which method suits best for a particular problem and/or data set
• can derive learning algorithms for new models and analyze new data with them.

General competence

Upon successful completion of the course, the student:

• can apply different predictive models on data and assess their performance
• can use supervised and unsupervised learning in different real life problem

Studenten skal etter å ha fullført emnet ha følgende totale læringsutbytte definert i kunnskap, ferdigheter og generell kompetanse:

Kunnskap

Studenten har

• omfattende kunnskap om relevante teorier relatert til team
• solid forståelse knyttet til hvordan team etableres og utformes i ulike situasjoner
• inngående kunnskap om psykologiske prosesser i team
• spesialisert kunnskap om teorier relatert til teameffektivitet og beslutningstaking i grupper og team
• avansert kunnskap om omstendigheter som hindrer eller øker gruppeeffektivitet og prestasjoner

Ferdigheter

Studenten

• har en bevisst forståelse av hvordan effektive teamprosesser foregår
• kan anvende forskningsbaserte teorier om team i egen praksis
• kan reflektere om egen atferd i gruppearbeid

Generell kompetanse

Studenten kan

• analysere og håndtere gjennomføring av effektive teamprosesser
• beherske fagområdets uttrykksformer
• reflektere og lære fra erfaringer med teamarbeid

Upon successful completion of the course, the candidate:

Knowledge

• has knowledge of problems within graphics and imaging that are applicable to machine learning, such as classification, segmentation, correspondence detection, and shape retrieval.
• has a good understanding of problems related to 3D shape and image synthesis.

Skills

• is able to apply state-of-the art machine learning algorithms to real-world problems related to imaging and 3D graphics.

Competence

• is aware of the state of the art in algorithms for machine learning on 3D data.
• has experience with real world problems within the course domain, with a focus on solutions using deep neural architectures.

For å kunne framstille seg til eksamen må studenten ha følgende godkjente arbeidskrav:

• Arbeidskrav 1: Deltakelse på spesifikke aktiviteter knyttet til gruppeoppstart og dynamikk som skal gjennomføres for å knytte teori fra emne opp mot å gjøre praktiske erfaringer.
• Arbeidskrav 2: To gruppeinnleveringer med et omfang hver på tilsvarende 1500 ord.

Arbeidskravene må være gjennomført og godkjent innen fastlagt frist for at studenten skal kunne framstille seg til eksamen. Dersom et eller flere arbeidskrav ikke blir godkjent, gis det anledning til å kunne levere en forbedret versjon én gang innen angitt frist.

The following required coursework must be approved before the student can take the exam:

Two mandatory group assignments consisting of technical tasks, summarized in reports (about 10 pages each).

The exam consists of three parts:

1. Oral presentation of 15 minutes (20% of the final grade), individual or in a group of two
2. Written evaluation of another student presentation, 500-1000 words (10% of the final grade), individual or in a group of two
3. Final project report between 6000 and 11,000 words (70% of the final grade), individual or in a group of two.

All three parts of the exam must be passed in order to pass the course.

The oral examination cannot be appealed.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for registering for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

Alle hjelpemidler er tillatt så lenge regler om kildehenvisning følges.

Grade scale A-F.